Technology is an elastic concept. What that means is that one technological solution may be used to complete an entirely different task, often with users “piggy-backing” on the technology they are borrowing. Commonly referred to as ‘technology transfer’ or ‘transfer of technology (TOT)’, the process can include not only technologies, but also anything from skills, knowledge, methods of manufacturing, samples of manufacturing and facilities, which are then transferred among governments, universities, and other institutions that ensure scientific and technological developments are accessible to a wider range of users who can further develop the technology into new products, processes, and applications (http://en.wikipedia.org/wiki/Technology_transfer).

While that may be a pretty broad and esoteric concept to wrap your head around, chances are you’ve utilized something that was a product of TOT. For instance, if you’ve ever used GPS on your mobile phone or in the car, the technology originally started out with military applications for the Navy to pinpoint submarines for missile launches under the U.S. Department of Defense. Now, it is used to find your nearest Taco Bell. Thanks TOT!

The translation industry is no exception to using technical transference. In fact, many current applications utilize transferred technology, especially as building blocks for many platforms and programs. Take, for instance, machine translation tools such as Google Translate and Bing Translator. The machine translation tools of today would not be possible if it wasn’t for predecessors dating back to the 1950’s.

According to Wikipedia, the first researcher in the field, Yehosha Bar-Hillel, began his research at MIT (1951). A Georgetown University MT research team followed (1951) with a public demonstration of its Georgetown-IBM experiment system in 1954. MT research programs popped up in Japan and Russia (1955), and the first MT conference was held in London (1956). Researchers continued to join the field as the Association for Machine Translation and Computational Linguistics was formed in the U.S. (1962) and the National Academy of Sciences formed the Automatic Language Processing Advisory Committee (ALPAC) to study MT (1964). According to a 1972 report by the Director of Defense Research and Engineering (DDR&E), the feasibility of large-scale MT was reestablished by the success of the Logos MT system in translating military manuals into Vietnamese during that conflict.

The French Textile Institute also used MT to translate abstracts from and into French, English, German and Spanish (1970); Brigham Young University started a project to translate Mormon texts by automated translation (1971); and Xerox used SYSTRAN to translate technical manuals (1978). Beginning in the late 1980s, as computational power increased and became less expensive, more interest was shown in statistical models for machine translation. Various MT companies were launched, including Trados (1984), which was the first to develop and market translation memory technology (1989). The first commercial MT system for Russian / English / German-Ukrainian was developed at Kharkov State University (1991).

MT on the web started with SYSTRAN Offering free translation of small texts (1996), followed by AltaVista Babelfish, which racked up 500,000 requests a day (1997). Franz-Josef Och (the future head of Translation Development AT Google) won DARPA’s speed MT competition (2003). More innovations during this time included MOSES, the open-source statistical MT engine (2007), a text/SMS translation service for mobiles in Japan (2008), and a mobile phone with built-in speech-to-speech translation functionality for English, Japanese and Chinese (2009). Recently, Google announced that Google Translate translates roughly enough text to fill 1 million books in one day (2012).

With so many versions of machine translation developed over the years, each piggy-backing on the others (utilizing both what worked and excluding what didn’t), it would be impossible to have the current models without its predecessors.

Even now, as programming becomes more and more shareable, current machine translation models are offering their functionality via APIs to allow other developers to use what they have themselves transferred from past iterations, in order to create new and exciting programs.

While translation may seem like a subject that is inherently archaic in its process, in fact, it is just as much prone to technical transference and innovation as something like GPS! All it takes is a little ingenuity.

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